Speaker
Description
With new specialisations such as Data Science driven by digitisation, efficiency potentials of a digital transformation are raised in both empirical research and data governance processes. Here, one challenge is to establish open and interoperable datasets, recognising the FAIR criteria (cf. Wilkinson et al., 2016) as a standard of that process. Data – as well as metadata – should comply to this standard. However, traditional methodological research processes (cf. Brosius, Haas, & Koschel, 2012, p. 28; Friedrichs, 1990, p. 119) lack the support of information technology which would lever the process into the digital age. Therefore, we propose a digital research process that closes ranks between the traditional process and the opportunities of a digital world.
The digital research process was established as a concept for a data model with corresponding roles (cf. Wuchner, & Sautter, 2020; Sautter, & Wuchner, 2020; Sautter et al., 2018). We found that a data governance process depends less on the specific method and much more on a common cross-method research process (cf. also UK Data Service). As a result, the digital research process needed to be highly adaptive to the purposes of different kinds of research fields.
The digital research process we propose consists of nine activities, terminated by data filing points (DFPs). The obligatory DFPs consider projects that focus on data search, acquisition, and archiving only. The optional DFPs represent the process of obtaining new (research) data. Additionally, optional data analysis may play a role in projects that merely reuse existing data. The optional DFPs represent the adaptability of research objectives in humanities. Equally unique to the digital research process is the frequent update of metadata throughout the research cycle, to create FAIR metadata throughout the time frame of the research and data processing.
